1 What is Cancer?


1.2 Replication Crisis Excel Version


2 RStudio ile proje oluştur


3 R Notebook

3.1 R Notebook dökümanı oluşturma


3.2 R Notebook’tan html, pdf ve word oluşturma


3.3 RNotebook vs RMarkdown

https://youtu.be/zNzZ1PfUDNk


4 R Markdown

4.1 Hem kendi kodları hem de html kodları yazılabilir

https://rmarkdown.rstudio.com

What is R Markdown? from RStudio, Inc. on Vimeo.


4.2 R Markdown: The Definitive Guide

https://bookdown.org/yihui/rmarkdown/


4.3 R Markdown syntax

https://gist.github.com/MinhasKamal/7fdebb7c424d23149140


4.4 Remedy Package


4.4.1 Remedy


4.6 Render Markdown via code

inside R

markdown::markdownToHTML('markdown_example.md', 
'markdown_example.html')

command line

R -e "markdown::markdownToHTML('markdown_example.md',
'markdown_example.html')"

4.7 pandoc Rstudio integration

command line

export PATH=$PATH:/Applications/RStudio.app/Contents/MacOS/pandoc
R -e "rmarkdown::render('markdown_example.md')"

5 RMarkdown chunk içinde R kodlarını çalıştırma

{r, results='asis'}
 iris %>%
  tibble::as_tibble() %>%
  details::details(summary = 'tibble')

6 Metin arasında R kodlarını çalıştırma


7 Chunk Options

7.1 Global Options

{r global_options, include=FALSE}
knitr::opts_chunk$set(fig.width = 12,
                      fig.height = 8,
                      fig.path = 'Figs/',
                      echo = FALSE,
                      warning = FALSE,
                      message = FALSE,
                      error = FALSE,
                      eval = TRUE,
                      tidy = TRUE,
                      comment = NA)

7.2 Other Code Languages


8 R Markdown kod örneği

{r}
data("cancer")
cancer
foreign::write.foreign(df = cancer,
                        datafile = "data/cancer.sav",
                        codefile = "data/cancer.spo",
                        package = "SPSS"
                        )

9 R Markdown Paket Çağırma 📦

{r}
suppressPackageStartupMessages(library("tidyverse"))
suppressPackageStartupMessages(library("survival"))

9.1 Sık kullandığım paketler 📦

{tidyverse} {tidylog}

{lubridate} {janitor}

{readxl} {foreign}

{summarytools} {ggstatsplot} {tangram} {finalfit} {psycho} {jmv}

{survival} {survminer}

{report} {kableExtra}


10 R Markdown Veri Yükleme SPSS


11 R Markdown Veri Yükleme Excel


12 Veri Görüntüleme

{r}
View(mydata)
glimpse(mydata)

13 Veri Düzenleme

{r}
mydata <- janitor::clean_names(mydata)
{r}
mydata$sontarih <- janitor::excel_numeric_to_date(
  as.numeric(mydata$olum_tarihi)
  )

14 Recode

{r}
mydata$Outcome <- "Dead"
mydata$Outcome[mydata$olum_tarihi == "yok"] <- "Alive"
{r}
## Recoding mydata$cinsiyet into mydata$Cinsiyet
mydata$Cinsiyet <- recode(mydata$cinsiyet,
               "K" = "Kadin",
               "E" = "Erkek")
mydata$Cinsiyet <- factor(mydata$Cinsiyet)

15 Recode regular expression

{r recode TNM stage}
#pT2N0Mx -> 2
mydata$Tstage <- stringr::str_match(
  mydata$patolojik_evre, 
  paste('(.+)', "N", sep=''))[,2]
)

16 Recode regular expression case_when

{r recode TNM2}
mydata <- mydata %>% 
    mutate(
        T_stage = case_when(
            grepl(pattern = "T1", x = .$Tstage) == TRUE ~ "T1",
            grepl(pattern = "T2", x = .$Tstage) == TRUE ~ "T2",
            grepl(pattern = "T3", x = .$Tstage) == TRUE ~ "T3",
            grepl(pattern = "T4", x = .$Tstage) == TRUE ~ "T4",
            TRUE ~ "Tx"
        )
    )

17 Recode regular expression case_when

{r}
mydata <- mydata %>% 
    mutate(
TumorPDL1gr1 = case_when(
        t_pdl1 < 1 ~ "kucuk1",
        t_pdl1 >= 1 ~ "buyukesit1"
    )
    )

18 R Markdown Tanımlayıcı İstatistikler

{r}
library(summarytools)
view(dfSummary(colon_s))

A beginner kit for #rstats The Landscape of R Packages for Automated Exploratory Data Analysis https://journal.r-project.org/archive/2019/RJ-2019-033/

@article{RJ-2019-033, author = {Mateusz Staniak and Przemysław Biecek}, title = {{The Landscape of R Packages for Automated Exploratory Data Analysis}}, year = {2019}, journal = {{The R Journal}}, doi = {10.32614/RJ-2019-033}, url = {https://journal.r-project.org/archive/2019/RJ-2019-033/index.html} }


18.1 Table One

{r, results='asis'}
# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- tableby(~ Cinsiyet + 
Yas + 
TumorYerlesimi
                ,
                data = mydata)
summary(tab1)

18.3 Kategorik Veriler

{r}
mydata %>% 
  janitor::tabyl(Categorical) %>%
  adorn_pct_formatting(rounding = 'half up',
                       digits = 1) %>%
  knitr::kable()
{r crosstable}
mydata %>%
    summary_factorlist(dependent = dependent, 
                       explanatory = explanatory,
                       total_col = TRUE,
                       p = TRUE,
                       add_dependent_label = TRUE) -> table
knitr::kable(table, row.names = FALSE, align = c('l', 'l', 'r', 'r', 'r'))

18.4 Kategorik Veriler için Grafikler

{r ggstatplot, layout='l-page'}
mydata %>% 
    ggstatsplot::ggbarstats(data = .,
                            main = Categorical_variable,
                            condition =  dependent_variable
                            )

18.5 Continious Variables

{r}
mydata %>% 
jmv::descriptives(
    data = .,
    vars = c(yas),
    hist = TRUE,
    dens = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sd = TRUE,
    variance = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE)

19 R Markdown örneği Çapraz Tablolar

{r crosstable}
library(finalfit)
mydata %>%
    summary_factorlist(dependent = dependent, 
                       explanatory = explanatory,
                       column = TRUE,
                       total_col = TRUE,
                       p = TRUE,
                       add_dependent_label = TRUE,
                       na_include=FALSE
                       # catTest = catTestfisher
                       ) -> table
knitr::kable(table,
             row.names = FALSE,
             align = c('l', 'l', 'r', 'r', 'r'))

20 R Markdown örneği Sağkalım

20.1 Sağkalım için veriyi düzenleme

{r define survival time}
mydata$int <- lubridate::interval(
  lubridate::ymd(mydata$CerrahiTarih),
  lubridate::ymd(mydata$SonTarih)
  )
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)
{r}
## Recoding mydata$Outcome into mydata$Outcome2
mydata$Outcome2 <- recode(mydata$Outcome,
               "Alive" = "0",
               "Dead" = "1")
mydata$Outcome2 <- as.numeric(mydata$Outcome2)

20.2 Kaplan-Meier

{r Kaplan-Meier}
mydata %>%
  finalfit::surv_plot(dependent,
                      explanatory,
                      xlab='Time (months)',
                      pval=TRUE,
                      legend = 'none',
                      break.time.by = 12,
                      xlim = c(0,60),
                      legend.labs = c('a','b')
)

20.3 Sağkalım Tabloları

{r}
km_fit <- survfit(dependent ~ explanatory,
                  data = mydata)
km_fit
{r, eval=FALSE, include=FALSE}
library(survival)
km <- with(mydata, Surv(OverallTime, Outcome2))
# head(km,80)
# plot(km)
{r 1-3-5-yr}
summary(km_fit, times = c(12,36,60))

20.4 Pairwise comparison

{r}
survminer::pairwise_survdiff(formula = Surv(time, Outcome) ~ Group, 
                             data = mydata,
                             p.adjust.method = "BH")

20.5 Multivariate Analysis Survival

{r Multivariate Analysis, eval=FALSE, include=FALSE}
library(finalfit)
library(survival)
explanatoryMultivariate <- explanatoryKM
dependentMultivariate <- dependentKM
mydata %>%
  finalfit(dependentMultivariate, explanatoryMultivariate) -> tMultivariate
knitr::kable(tMultivariate, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))

21 jamovi

21.1 jamovi ve R entegrasyonu

Rj Editor – Analyse your data with R in jamovi


21.2 {jmv} paket kodları

jamovi syntax mode


22 Güncellemeler olunca kodlar çalışacak mı?


22.1 Paket Kütüphaneleri

  • packrat / renv

https://environments.rstudio.com


22.2 Docker

  • docker

22.2.1 The Rocker Project

Docker Containers for the R Environment

docker run --rm -ti rocker/r-base

Or get started with an RStudio® instance:

docker run -e PASSWORD=yourpassword --rm -p 8787:8787 rocker/rstudio

and point your browser to localhost:8787 Log in with user/password rstudio/yourpassword


Managing containers


22.3 Yeni R sürümleri

  • RSwitch

https://rud.is/rswitch/

  • Using RSwitch

https://rud.is/rswitch/guide/

: scale 30%


23 Yedeklemeyi nasıl yapacağız

23.1 Projeyi düzgün organize edin

  • pdf
  • R
  • images
  • bib
{r load library}
source(file = here::here("R", "loadLibrary.R"))

23.2 Save Final Data

{r}
saved data after analysis to `mydata.xlsx`.

save.image(file = here::here("data", "mydata_work_space.RData"))

readr::write_rds(x = mydata, path = here::here("data", "mydata_afteranalysis.rds"))

saveRDS(object = mydata, file = here::here("data", "mydata.rds"))

writexl::write_xlsx(mydata, here::here("data", "mydata.xlsx"))

paste0(rownames(file.info(here::here("data", "mydata.xlsx"))), " : ", file.info(here::here("data", "mydata.xlsx"))$ctime)

23.3 GitHub Yedekleme


24 Her dökümanın sonuna kullandığınız kütüphaneler için atıf yazdırabilirsiniz

{r}
citation()

24.1 Libraries Used

citation()

24.2 Bu oturuma spesifik kullanılan paketler


24.3 Tek tek paket atıfları

{r library citations}
citation("tidyverse")
citation("readxl")
citation("janitor")
citation("report")
citation("finalfit")
citation("ggstatplot")

24.4 Jamovi ve R için atıf örneği


25 Her dökümanın sonuna oturum detaylarınızı yazdırabilirsiniz

{r session info, echo=TRUE}
sessionInfo()

25.1 Session Info

sessionInfo()

26 Sonraki Konular

  • RStudio ile GitHub kullanımı

29 Geri Bildirim



---
title: What is Cancer?
author: "[Serdar Balcı, MD, Pathologist](https://sbalci.github.io/)"
institute: "[serdarbalci.com](https://www.serdarbalci.com) [patolojinotlari.com](https://www.patolojinotlari.com)"
date: "`r format(Sys.Date())`"
output:
  revealjs::revealjs_presentation:
    keep_md: true
    incremental: true
    theme: sky
    highlight: pygments
    center: false
    smart: true
    transition: fade
    self_contained: true
    ig_width: 7
    fig_height: 6
    fig_caption: true
    reveal_options:
      slideNumber: true
      previewLinks: true
  prettydoc::html_pretty:
    keep_md: true
    theme: leonids
    highlight: github
  rmdshower::shower_presentation:
    keep_md: true
  html_notebook:
    fig_caption: yes
    highlight: kate
    number_sections: yes
    theme: flatly
    toc: yes
    toc_depth: 5
    toc_float: yes
  xaringan::moon_reader:
    keep_md: true
    lib_dir: libs
    nature:
      beforeInit: ["macros.js", "https://platform.twitter.com/widgets.js"]
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
    self_contained: true
  pdf_document:
    keep_md: true
    toc: yes
    toc_depth: '5'
  html_document:
    fig_caption: yes
    keep_md: yes
    toc: yes
    toc_depth: 5
    toc_float: yes
editor_options: 
  chunk_output_type: inline
---


<!-- Open all links in new tab-->  
<base target="_blank"/>  


<!-- Go to www.addthis.com/dashboard to customize your tools --> <script type="text/javascript" src="//s7.addthis.com/js/300/addthis_widget.js#pubid=ra-5bc36900a405090b">  
</script>



```{r global_options, include=FALSE}
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figs/', echo = FALSE, warning = FALSE, message = FALSE, error = FALSE, eval = TRUE, tidy = TRUE, comment = NA, cache = TRUE)
```



```{r xaringan, eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# xaringan::inf_mr()
# servr::daemon_stop(1)
```



# What is Cancer?






---


## Replication Crisis



https://en.wikipedia.org/wiki/Replication_crisis


---

## Replication Crisis Excel Version




---

# RStudio ile proje oluştur







---

# R Notebook  

## R Notebook dökümanı oluşturma 




---

## R Notebook'tan html, pdf ve word oluşturma  





---

## RNotebook vs RMarkdown  

<iframe width="560" height="315" src="https://www.youtube.com/embed/zNzZ1PfUDNk" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>  

https://youtu.be/zNzZ1PfUDNk


---

# R Markdown

## Hem kendi kodları hem de html kodları yazılabilir

https://rmarkdown.rstudio.com

<iframe src="https://player.vimeo.com/video/178485416?color=428bca&title=0&byline=0&portrait=0" width="640" height="400" frameborder="0" allow="autoplay; fullscreen" allowfullscreen></iframe>
<p><a href="https://vimeo.com/178485416">What is R Markdown?</a> from <a href="https://vimeo.com/rstudioinc">RStudio, Inc.</a> on <a href="https://vimeo.com">Vimeo</a>.</p>

---

## R Markdown: The Definitive Guide

https://bookdown.org/yihui/rmarkdown/


---

## R Markdown syntax

https://gist.github.com/MinhasKamal/7fdebb7c424d23149140


<script src="https://gist.github.com/MinhasKamal/7fdebb7c424d23149140.js"></script>



---

## Remedy Package  

[<img src="https://raw.githubusercontent.com/ThinkR-open/remedy/master/reference/figures/thinkr-hex-remedy.png" width=250px>](https://github.com/ThinkR-open/remedy)


---

### Remedy  

[<img src="https://raw.githubusercontent.com/ThinkR-open/remedy/master/reference/figures/remedy_example.gif" width=500px>](https://github.com/ThinkR-open/remedy)


---

## R Markdown paket ve şablonları  

https://bookdown.org/yihui/rmarkdown/document-templates.html





---

## Render Markdown via code

*inside R*

```
markdown::markdownToHTML('markdown_example.md', 
'markdown_example.html')
```

*command line*

```
R -e "markdown::markdownToHTML('markdown_example.md',
'markdown_example.html')"
```

---


## pandoc Rstudio integration

*command line*

```
export PATH=$PATH:/Applications/RStudio.app/Contents/MacOS/pandoc
```


```
R -e "rmarkdown::render('markdown_example.md')"
```


---

# RMarkdown `chunk` içinde `R` kodlarını çalıştırma


```
{r, results='asis'}
 iris %>%
  tibble::as_tibble() %>%
  details::details(summary = 'tibble')
```

---

# Metin arasında `R` kodlarını çalıştırma





---

# Chunk Options

## Global Options

```
{r global_options, include=FALSE}
knitr::opts_chunk$set(fig.width = 12,
                      fig.height = 8,
                      fig.path = 'Figs/',
                      echo = FALSE,
                      warning = FALSE,
                      message = FALSE,
                      error = FALSE,
                      eval = TRUE,
                      tidy = TRUE,
                      comment = NA)
```

---

## Other Code Languages


[![](https://d33wubrfki0l68.cloudfront.net/162347ef5afe219da22fb7d7d9a5989f2c3e5a85/59316/lesson-images/languages-1-demos.png)](https://rmarkdown.rstudio.com/lesson-5.html)


---


# R Markdown kod örneği  


```
{r}
data("cancer")
cancer
foreign::write.foreign(df = cancer,
                        datafile = "data/cancer.sav",
                        codefile = "data/cancer.spo",
                        package = "SPSS"
                        )
```


---

# R Markdown Paket Çağırma 📦    


```
{r}
suppressPackageStartupMessages(library("tidyverse"))
suppressPackageStartupMessages(library("survival"))
```

---

## Sık kullandığım paketler 📦  

{tidyverse}
{tidylog}

{lubridate}
{janitor}

{readxl}
{foreign}

{summarytools}
{ggstatsplot}
{tangram}
{finalfit}
{psycho}
{jmv}

{survival}
{survminer}

{report}
{kableExtra}

---

# R Markdown Veri Yükleme SPSS  




---

# R Markdown Veri Yükleme Excel  






---

# Veri Görüntüleme


```
{r}
View(mydata)
glimpse(mydata)
```




---

# Veri Düzenleme

```
{r}
mydata <- janitor::clean_names(mydata)
```

```
{r}
mydata$sontarih <- janitor::excel_numeric_to_date(
  as.numeric(mydata$olum_tarihi)
  )
```


---

# Recode


```
{r}
mydata$Outcome <- "Dead"
mydata$Outcome[mydata$olum_tarihi == "yok"] <- "Alive"
```


```
{r}
## Recoding mydata$cinsiyet into mydata$Cinsiyet
mydata$Cinsiyet <- recode(mydata$cinsiyet,
               "K" = "Kadin",
               "E" = "Erkek")
mydata$Cinsiyet <- factor(mydata$Cinsiyet)
```


---

# Recode regular expression


```
{r recode TNM stage}
#pT2N0Mx -> 2
mydata$Tstage <- stringr::str_match(
  mydata$patolojik_evre, 
  paste('(.+)', "N", sep=''))[,2]
)
```


---

# Recode regular expression case_when

```
{r recode TNM2}
mydata <- mydata %>% 
    mutate(
        T_stage = case_when(
            grepl(pattern = "T1", x = .$Tstage) == TRUE ~ "T1",
            grepl(pattern = "T2", x = .$Tstage) == TRUE ~ "T2",
            grepl(pattern = "T3", x = .$Tstage) == TRUE ~ "T3",
            grepl(pattern = "T4", x = .$Tstage) == TRUE ~ "T4",
            TRUE ~ "Tx"
        )
    )
```

---

# Recode regular expression case_when

```
{r}
mydata <- mydata %>% 
    mutate(
TumorPDL1gr1 = case_when(
        t_pdl1 < 1 ~ "kucuk1",
        t_pdl1 >= 1 ~ "buyukesit1"
    )
    )
```

---

# R Markdown Tanımlayıcı İstatistikler  


```
{r}
library(summarytools)
view(dfSummary(colon_s))
```


---


A beginner kit for #rstats
The Landscape of R Packages for Automated Exploratory Data Analysis
https://journal.r-project.org/archive/2019/RJ-2019-033/



@article{RJ-2019-033,
  author = {Mateusz Staniak and Przemysław Biecek},
  title = {{The Landscape of R Packages for Automated Exploratory Data
          Analysis}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-033},
  url = {https://journal.r-project.org/archive/2019/RJ-2019-033/index.html}
}



---


## Table One  


```
{r, results='asis'}
# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- tableby(~ Cinsiyet + 
Yas + 
TumorYerlesimi
                ,
                data = mydata)
summary(tab1)
```

---

## The Grammar of Tables


[tangram: The Grammar of Tables](https://cran.r-project.org/web/packages/tangram/)

[A grammar of tables](https://github.com/leeper/tttable)

[Grammar of Tables?](https://gist.github.com/leeper/f9cfbe6bd185763762e126a4d8d7c286)

[Easily generate information-rich, publication-quality tables from R](https://gt.rstudio.com)


---

## Kategorik Veriler

```
{r}
mydata %>% 
  janitor::tabyl(Categorical) %>%
  adorn_pct_formatting(rounding = 'half up',
                       digits = 1) %>%
  knitr::kable()
```

```
{r crosstable}
mydata %>%
    summary_factorlist(dependent = dependent, 
                       explanatory = explanatory,
                       total_col = TRUE,
                       p = TRUE,
                       add_dependent_label = TRUE) -> table
knitr::kable(table, row.names = FALSE, align = c('l', 'l', 'r', 'r', 'r'))
```

---

## Kategorik Veriler için Grafikler  

```
{r ggstatplot, layout='l-page'}
mydata %>% 
    ggstatsplot::ggbarstats(data = .,
                            main = Categorical_variable,
                            condition =  dependent_variable
                            )
```

---


## Continious Variables

```
{r}
mydata %>% 
jmv::descriptives(
    data = .,
    vars = c(yas),
    hist = TRUE,
    dens = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sd = TRUE,
    variance = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE)
```

---

# R Markdown örneği Çapraz Tablolar  

```
{r crosstable}
library(finalfit)
mydata %>%
    summary_factorlist(dependent = dependent, 
                       explanatory = explanatory,
                       column = TRUE,
                       total_col = TRUE,
                       p = TRUE,
                       add_dependent_label = TRUE,
                       na_include=FALSE
                       # catTest = catTestfisher
                       ) -> table
knitr::kable(table,
             row.names = FALSE,
             align = c('l', 'l', 'r', 'r', 'r'))
```

---

# R Markdown örneği Sağkalım  

- Drawing Survival Curves Using ggplot2  
https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html

## Sağkalım için veriyi düzenleme

```
{r define survival time}
mydata$int <- lubridate::interval(
  lubridate::ymd(mydata$CerrahiTarih),
  lubridate::ymd(mydata$SonTarih)
  )
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)
```

```
{r}
## Recoding mydata$Outcome into mydata$Outcome2
mydata$Outcome2 <- recode(mydata$Outcome,
               "Alive" = "0",
               "Dead" = "1")
mydata$Outcome2 <- as.numeric(mydata$Outcome2)
```

---

## Kaplan-Meier

```
{r Kaplan-Meier}
mydata %>%
  finalfit::surv_plot(dependent,
                      explanatory,
                      xlab='Time (months)',
                      pval=TRUE,
                      legend = 'none',
                      break.time.by = 12,
                      xlim = c(0,60),
                      legend.labs = c('a','b')
)
```

---

## Sağkalım Tabloları

```
{r}
km_fit <- survfit(dependent ~ explanatory,
                  data = mydata)
km_fit
```

```
{r, eval=FALSE, include=FALSE}
library(survival)
km <- with(mydata, Surv(OverallTime, Outcome2))
# head(km,80)
# plot(km)
```

```
{r 1-3-5-yr}
summary(km_fit, times = c(12,36,60))
```

---

## Pairwise comparison

```
{r}
survminer::pairwise_survdiff(formula = Surv(time, Outcome) ~ Group, 
                             data = mydata,
                             p.adjust.method = "BH")
```

---

## Multivariate Analysis Survival

```
{r Multivariate Analysis, eval=FALSE, include=FALSE}
library(finalfit)
library(survival)
explanatoryMultivariate <- explanatoryKM
dependentMultivariate <- dependentKM
mydata %>%
  finalfit(dependentMultivariate, explanatoryMultivariate) -> tMultivariate
knitr::kable(tMultivariate, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
```

---

# jamovi

## jamovi ve R entegrasyonu

[Rj Editor – Analyse your data with R in jamovi](https://blog.jamovi.org/2018/07/30/rj.html)



---

## {jmv} paket kodları

[jamovi syntax mode](https://www.jamovi.org/user-manual.html#syntax-mode)






---

# Güncellemeler olunca kodlar çalışacak mı?


---

## Paket Kütüphaneleri

- packrat / renv

https://environments.rstudio.com

---

## Docker

- docker

---

### The Rocker Project

Docker Containers for the R Environment

```
docker run --rm -ti rocker/r-base
```

Or get started with an RStudio® instance:

```
docker run -e PASSWORD=yourpassword --rm -p 8787:8787 rocker/rstudio
```

and point your browser to [localhost:8787](localhost:8787)
Log in with user/password rstudio/yourpassword 

---



[Managing containers](https://www.rocker-project.org/use/managing_containers/)


---

## Yeni R sürümleri 

- RSwitch

https://rud.is/rswitch/

- Using RSwitch

https://rud.is/rswitch/guide/


![: scale 30%](https://rud.is/rswitch/guide/menu-info.png)


---

# Yedeklemeyi nasıl yapacağız

## Projeyi düzgün organize edin

- pdf
- R
- images
- bib

```
{r load library}
source(file = here::here("R", "loadLibrary.R"))
```

---

## Save Final Data

```
{r}
saved data after analysis to `mydata.xlsx`.

save.image(file = here::here("data", "mydata_work_space.RData"))

readr::write_rds(x = mydata, path = here::here("data", "mydata_afteranalysis.rds"))

saveRDS(object = mydata, file = here::here("data", "mydata.rds"))

writexl::write_xlsx(mydata, here::here("data", "mydata.xlsx"))

paste0(rownames(file.info(here::here("data", "mydata.xlsx"))), " : ", file.info(here::here("data", "mydata.xlsx"))$ctime)

```




---

## GitHub Yedekleme

```{r github push, eval=FALSE, include=FALSE}
CommitMessage <- paste("updated on ", Sys.time(), sep = "")
wd <- getwd()
gitCommand <- paste("cd ", 
                    wd,
                    " \n git add . \n git commit --message '",
                    CommitMessage,
                    "' \n git push origin master \n",
                    sep = ""
                    )
system(command = gitCommand,
       intern = TRUE
)
```

---

# Her dökümanın sonuna kullandığınız kütüphaneler için atıf yazdırabilirsiniz

```
{r}
citation()
```

---

## Libraries Used  

```{r library citation, echo=TRUE}
citation()
```

---

## Bu oturuma spesifik kullanılan paketler  

```{r library citation as report, eval=FALSE, include=FALSE, results='asis'}
report::cite_packages(session = sessionInfo())
```


---

## Tek tek paket atıfları


```
{r library citations}
citation("tidyverse")
citation("readxl")
citation("janitor")
citation("report")
citation("finalfit")
citation("ggstatplot")
```


---

## Jamovi ve R için atıf örneği

- The jamovi project (2019). jamovi. (Version 0.9) [Computer Software]. Retrieved from https://www.jamovi.org.

- R Core Team (2018). R: A Language and envionment for statistical computing. [Computer software]. Retrieved from https://cran.r-project.org/.

- Fox, J., & Weisberg, S. (2018). car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car.



---

# Her dökümanın sonuna oturum detaylarınızı yazdırabilirsiniz  

```
{r session info, echo=TRUE}
sessionInfo()
```

---

## Session Info

```{r session info, echo=TRUE}
sessionInfo()
```

---

# Sonraki Konular

- RStudio ile GitHub kullanımı
- ...

---

# Önerilen Kaynaklar

- [Reproducible Templates for Analysis and Dissemination](https://www.coursera.org/learn/reproducible-templates-analysis/home/info)

- [Happy Git and GitHub for the useR](https://happygitwithr.com/)





---

# Sunum Linkleri

https://sbalci.github.io/MyRCodesForDataAnalysis/R-Markdown.nb.html
https://sbalci.github.io/MyRCodesForDataAnalysis/R-Markdown.html

https://forms.gle/UqGJBiAjB8uLPRon8

---

# Geri Bildirim

- Geri bildirim için tıklayınız: _[Geri bildirim formu](https://goo.gl/forms/YjGZ5DHgtPlR1RnB3)_


---

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---

# İletişim  

Completed on `r Sys.Date()`.  

Serdar Balci, MD, Pathologist  
drserdarbalci@gmail.com  

https://rpubs.com/sbalci/CV   
https://sbalci.github.io/  
https://github.com/sbalci  
https://twitter.com/serdarbalci


